Phenotyping congenital anomalies in administrative hospital records

Ania Zylbersztejn1,2, Maximiliane Verfürden1,2, Pia Hardelid1,2

  • 1Population, Policy and Practice Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK.

Insights

Congenital anomaly prevalence and outcomes differ significantly based on the diagnostic code list used in administrative health records. Researchers must consider code list variations when analyzing data on childhood congenital anomalies.

Area of Science:

  • Pediatric Health Research
  • Public Health Surveillance
  • Health Informatics

Background:

  • Congenital anomalies are a significant cause of childhood co-morbidity.
  • Administrative health records are increasingly utilized for identifying congenital anomalies.
  • Comparability of diagnostic code lists for congenital anomaly identification is not well-established.

Purpose of the Study:

  • To compare the prevalence of congenital anomalies in children identified through administrative health records.
  • To assess prognostic outcomes for children with congenital anomalies across different diagnostic code lists.
  • To evaluate the impact of using distinct code lists on identifying congenital anomaly cases and associated risks.

Main Methods:

  • National cohorts of livebirths in England (2003-2014) and Scotland (2003-2011) were established.
  • Congenital anomalies were identified using diagnosis codes recorded at birth, during hospitalization, or as a cause of death before age two.
  • Three code lists were compared: EUROCAT, Hardelid, and Feudtner.

Main Results:

  • Prevalence varied by code list: EUROCAT (3.7-4.1%), Hardelid (3.0-3.1%), and Feudtner (1.5-1.8%).
  • Postnatal hospital admissions ranged from 65.2% to 77.0% across code lists and countries.
  • Mortality rates per 1000 births varied significantly, with Feudtner showing the highest risk and EUROCAT the lowest.

Conclusions:

  • The prevalence of congenital anomalies identified in administrative data is highly dependent on the code list used.
  • Differences in prevalence and outcomes reflect variations in coding practices and admission thresholds.
  • Researchers should conduct sensitivity analyses using multiple code lists when studying congenital anomalies in administrative health data.
Abstract

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